Translanguaging and the No Voice Policy in L2 Sign Language Contexts
Bibliographic record
Abstract
This article draws on translanguaging theory and research to consider a common pedagogical practice in American Sign Language (ASL) as a second language (L2) classroom, the No Voice policy (i.e., spoken language use is forbidden). The No Voice policy serves important cultural and practical purposes, but by nature limits learners’ access to their entire linguistic repertoire, which raises questions about the overall impact of the policy on learners’ language development. Current literature about pedagogical translanguaging has not yet addressed practices that integrate (and, by extension, limit) selective modalities; we evaluate this gap and propose several directions for future research on the topic.Moreover, previous discussions of translanguaging practices involving recognized minority (e.g., Basque, Welsh, Irish) spoken languages are not wholly comparable to sign languages, which are not yet official or fully recognized languages in most countries and are therefore additionally vulnerable.We take into account the impact of ASL L2 learners on the language community, as many learners go on to become interpreters and allies to the deaf community. Keywords: American Sign Language as a second language, hearing adult learners, selective modality, pedagogical translanguaging, minority language
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".